DETERMINANTS OF BANK PERFORMANCE ON ASIAN …

DETERMINANTS OF BANK PERFORMANCE ON ASIAN

COMMERCIAL BANKS

JATUPORN SANMONTRIKUL

MASTER OF SCIENCE PROGRAM IN FINANCE

(INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY, BANGKOK, THAILAND

MAY 2013


COMMERCIAL BANKS

JATUPORN SANMONTRIKUL

MASTER OF SCIENCE PROGRAM IN FINANCE

(INTERNATIONAL PROGRAM)

FACULTY OF COMMERCE AND ACCOUNTANCY

THAMMASAT UNIVERSITY, BANGKOK, THAILAND

MAY 2013


COMMERCIAL BANKS

Jatuporn Sanmontrikul

An Independent Study

Submitted in Partial Fulfillment of the Requirements

for the Degree of Master of Science (Finance)

Master of Science Program in Finance

(International Program)

Faculty of Commerce and Accountancy

Thammasat University, Bangkok, Thailand

May 2013

Thammasat University

Faculty of Commerce and Accountancy

An Independent Study

By

Jatuporn Sanmontrikul

'oDeterminants of Bank Performance on Asian Commercial Banks"

has been approved as a partial fulfillment of the requirements

for the Degree of Master of Science (Finance)

On May,2013

Advisor: //r/m[* {4(Assoc. Prof. Dr. Kulpatra Sirodom)

GP"*v"4''Co-Advisor: ....... .............

(Assoc. Prof. Dr. Bhanupong Nidhiprabha)

DETERMINANTS OF BANK PERFORMANCE ON ASIAN COMMERCIA L BANKS

ABSTRACT

This study examines the determinants of bank performance across five Asian countries; they

are Hong Kong, Thailand, Indonesia, Malaysia and Philippines from 2004 to 2011. The empirical

evidence indicates that an increase in equity to total assets is beneficial to sustain capital strength and

higher level of bank profitability. Increased loans to total assets ratio leads to a decline in bank profit

as a result of lower credit quality and lower rate of return. Non-performing loans to gross loans lead to

lower bank profit because of higher probability credit defaults from lending activities. Also, higher

total assets may result in lower bank profit if banks cannot exploit the economies of scale. In addition,

inflation rate increases operating costs faster than revenue. Higher GDP growth help generate more

profits to bank. Furthermore, Thai banks profitability management is higher than Philippine banks and

they extract higher interest rate spread than Hong Kong banks. However, the findings investigate that

Indonesia banks can gain more interest rate spread because of low competition when compared with

Thai banks. Subsequently, the empirical results show that the effect of the 2008 global financial crisis

reduced return on equity and return on assets, with slightly wider net interest margin.

1

I. INTRODUCTION

Banking sector plays a key role in the financial sector as an intermediary between borrowers

and lenders. The depositing and lending operations are the essential activities in the household

consumption and company’s investments parts on both domestic and foreign transactions that have

significant impact on economic growth and stability. In addition, banking sector is also an important

component of financial industry to drive the financial system for economy.

Bank performance could be indicated by a simple accounting estimation to determine how

well bank can reach a goal during a given period, regularly an annual accounting period. The study on

the determinants of bank performances hopefully will bring the benefit directly to participated

stakeholders or to other extents, indirectly to the banks such as depositors, borrowers, investors, bank

managers, employees and regulators, etc. Furthermore, the research aims to explore the degree of

profitability between Asian commercial banks. This report mainly focuses on the profit side of the

bank performances among Asian commercial banks in order to clarify what are the determinants that

explain the bank performances by using return on equity, return on asset and net interest margin

across countries.

Finally, the study is categorized into six sections as follows; the literature review of the

determinants of bank performance is shown in section II, the theoretical frameworks are represented

in section III. Next, illustration of the data descriptions of Asian banks are provided in section IV.

Then, research methodologies and results are explained in section V. Subsequently, the conclusion is

briefly presented in Section VI.

2

II. LITERATURE REVIEW

There are various studies on bank performances in several countries which aim to explain the

importance of factors and effects on bank performances. Moreover, bank analysis is usually reviewed

into several types of bank performance such as efficiency, cost, profit and market structure which are

depended upon data selection, assumption and hypothesis.

However, there are only few studies that explore and compare bank performances across

Asian countries whereas most studies usually provide bank performance in a single country instead.

For Asian countries, Kwan, S. (2003) investigated the bank performance that focused on the operation

side from 1992 to 1999 and compared the effect from Asian crisis. The operating costs in Asian banks

had a tendency to decline continuously from 1992 to 1997. This implies that banks have been

improving over the period before the crisis. In addition, labor costs were able to reduce from 1997 to

1999 but less flexible to decrease in physical capital costs. Furthermore, Lee,C.C. and Hsieh,M.F.

(2013) also examined the impact of bank capital on profitability and risk in Asian countries. They

explained that bank capital as the proxy of equity to total assets ratio is positively significant to bank

profits, but negatively to bank risks. They also suggested that bank capital can provide an increase in

bank profit, but a decrease in bank risk after the crisis in 1997. Moreover, low-income countries have

a higher capital effect on profitability.

Regarding single Asian country, Heffernan,S. and Fu,M. (b2008) studied about bank

performance in China from 1999 to 2006. They found that measurement of economic value added and

net interest margin were better than ROE and ROA. Macroeconomic variables can provide significant

effects with the expected signs. The type of bank is significant, while its size is not, to investigate

that rural commercial banks outperform the four biggest and urban commercial banks because

they benefit from local monopoly.

In the country outside Asia like Demirgüç-Kunt, A. and Huizinga, H. (2000), they studied the

effect of financial structure on bank profitability. They focused on the impact of financial

development and financial structure on bank performance between OECD countries (Organisation for

3

Economic Co-operation and Development countries) and developing countries, approximately 90

percent of all bank assets worldwide. For countries with underdeveloped financial systems,

improvement of financial development increased efficiency and competitiveness of banking industry

although it may reduce bank margin and profit.

Table I provides some literature reviews on the relationship between bank performance and

its determinants. For example, Demirgüç-Kunt, A. and Huizinga, H. (2000) found that lagged equity,

non-interest earning assets and customer & short term funding to total assets are mostly statistically

significant to return on asset and net interest margin. In addition, inflation rate, GDP growth rate and

taxation rate are mostly statistically significant to them.

Table I: Literature reviews on the relationship between bank performance and its determinants.

Authors(Year)

Research Topic

Data range

Countries Mearure of bank profitabilities

Mostly significant bank’s specific variables

Mostly significant macro variables

Metho-dologies

Demirgüç-Kunt, A. and Huizinga, H., 2000.

Financial Structure and Bank Profitability

1990-1997

44 countries in OECD and developing countries

ROA and NIM

Lagged equity, Non-Interest earning assets and Customer and short term funding to total assets

Inflation rate, GDP growth rate, taxation rate

Panel data model

Heffernan, S. and Fu, M., 2008.

The determinants of bank performance in China

1999-2006

China NIM, ROAA, ROAE, EVA

Cost to income ratio, Loan loss reserves/gross loans

Annual unemploy-ment rate

Panel model with fixed effect

Kwan, S., 2003.

Operating performance of banks among Asian economies: An international and time series comparison

1992-1999

5 countries in Asia (Hong kong, South Korea, Malaysia, Singapore, Thailand)

operating cost, labor cost, physical capital cost

Retail deposits to total deposits, Cash and due from banks to total assets, Loans to total assets

Not included

Panel model with fixed effect

Lee,C.C. and Hsieh,M.F., 2013.

The impact of bank capital on profit-ability and risk in Asian banking

1994-2008

42 Asian banks

ROA, ROE, NIM, etc.

Loan loss Reserve to gross loans, Loans to total assets, Equity to total assets, Liquid assets to customer and short-term deposits

Inflation rate, Domestic credit to private sector, GDP growth rate, Real interest rate

Two GMM and Dynamic panel model

4

III. THEORETICAL FRAMEWORKS

The study categorized the variables into two main groups, the bank performance as

measurement of profitability and its determinants. The purpose of the study is to review factors that

determine the performance of the bank. Bank performances are set as dependent variables by focusing

on profitability using proxies as return on equity, return on assets and net interest margin. The

independent variables represent those bank’s specific characteristics as well as macroeconomics

variables by using accounting measurement which are beneficial to compare the degrees of

profitability among Asian banks. In addition, country dummy variables and time dummy variables are

also being used.

Table II represents the descriptive variables used in the regression models, consisting banks’

profitability with the proxy as return on equity, return on asset and net interest margin and its

determinants. Under the hypothesized relationship between dependent variables and independent

variables, the expectations of both positive and negative sign are generally in the bank’s specific

variables, except non-performing loans to gross loans, implying that these variables are more sensitive

to the change of the bank profitability.

Table II: Descriptive variables used in the regression models

Variables Descriptions Hypothesized relation to bank profitabilities

Dependent variables

ROE Return on equity NA

ROA Return on asset NA

NIM Net interest margin NA

Independent variables

Bank’s specific variables

ETA Equity to total assets +/-

LTA Loans to total assets +/-

NPL/GL Non-performing loans to gross loans -

LA/CS Liquid asset to Customer & short term funding

+/-

LNSIZE Logarithm on total assets +/-

5

Macroeconomics variables

INF Inflation rate -

GDPG Gross domestic product growth +

3.1 Measures of bank performance on the profitability

Bank’s performance variables

(1) ROE = equity Total

incomeNet

(1)

Return on equity (ROE) is equal to net income divided by total equity. ROE is used as the

measure of profit performance in order to see how much bank earns from its equity investments. If

ROE ratio is higher, it implies that banks have more chances to generate higher rate of return from

their equities and gain higher profits.

Figure1 represents statistical summary of means of return on equity across banks in five

countries from 2004 to 2011. It is obviously seen that most countries excluding Malaysia had a lower

return on equity in 2008. This suggests that financial institutions including commercial banks in Asia

had been impacted by the global financial crisis 2008. After this period, banks in each country had

been gradually improving their performance since 2009 to 2011. As we can see the level of

improvement from figure 2, Philippines showed sign of the fastest recovery from -10.26 %YoY in

2008 to 27.14 %YoY in the next year, this could be investigated that Philippines outperformed other

countries under the uncertainty level in the market during the crisis.

6

Source: Bankscope

Source: Bankscope

(2) ROA = asset Total

incomeNet

(2)

Return on asset (ROA) is equal to net income divided by total assets. ROA is a measure for

profit performance in order to see the bank's ability to manage the assets to generate income. If ROA

ratio is higher, it implies that the bank has more opportunity to produce higher profits from their

assets and portfolio managements.

0

5

10

15

20

25

30

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure1: Means of return on equity (ROE) among countries

TH HK ID MY PH

-60

-40

-20

0

20

40

60

80

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 %

YoY

)

Year

Figure 2: Percentage growth of return on equity (ROE) among countries

TH HK ID MY PH

7

Figure 3 presents statistical summary of means of return on asset across banks in five

countries from 2004 to 2011. The largest ROA was in Indonesia which peaked at 2.62 %YoY in 2004

compared to other countries over the sample period because of enhancement of asset qualities and

smaller assets which generate higher ratio and increase the level of profitability in the banking sector

(Bank Indonesia, 2005). Furthermore, the ratio dropped in 2008, then came back ahead again.

Nevertheless, in figure 4, Malaysia had lower influence than other countries during the 2008 crisis

with the rise on the percentage growth of ROA at 23.46 %YoY, suggesting that Malaysia kept up with

economic uncertainty better than other countries.

Source: Bankscope

Source: Bankscope

0

0.5

1

1.5

2

2.5

3

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure3: Means of return on asset (ROA) among countries

TH HK ID MY PH

-60

-40

-20

0

20

40

60

80

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 %

YoY

)

Year

Figure 4: Percentage growth of return on asset(ROA) among countries

TH HK ID MY PH

8

(3) NIM = assets Total

expensesInterest - incomesInterest

(3)

Net interest margin (NIM) is equal to the difference between interest incomes and interest

expenses divided by total assets. NIM is a measure of profit performance to see the bank's ability to

manage the assets and the liabilities. The higher NIM indicates that banks have lower cost of interest

expenses, also able to acquire more assets with higher interest incomes, thus they can generate more

profits.

Figure5 represents statistical summary of means of net interest margin across banks in five

countries from 2004 to 2011. It is shown that Indonesia has the highest net interest margin over the

period, followed by Philippines, Malaysia, Thailand and Hong Kong, respectively. However, all

countries had a slight difference in the amount of NIM during the global financial crisis 2008,

indicating that these countries excessively rely on short-term funding. (Arner, D.W. and Park, C.Y.,

2010). Nevertheless, figure 6 finds that NIM has decreased in most countries in term of percentage

growth. Interestingly, Thailand had the highest NIM ratio at about 23.03 %YoY during the crisis. This

implies that Thailand could gain more interest incomes from higher interest rates and higher lending

volumes compared to the other countries.

Source: Bankscope

0

1

2

3

4

5

6

7

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure5: Means of net interest margin (NIM) among countries

TH HK ID MY PH

9

Source: Bankscope

3.2 Determinants of bank performance on the profitability

Bank’s specific variables

(1) ETA = assets Total

Equity

(4)

Equity to total assets (ETA) is equal to book value of equity divided by total assets. It is a

popular measurement of capital adequacy on bank performance. Naceur, S.B. and Omran,M (2011)

and Berger(1995) suggested that if ETA ratio is higher, it indicates the bank's higher equity which

reduces the chance of leading that bank into bankruptcy and also hints to the lower cost of capital and

lower risk taking. This implies that ETA ratio should have a positive relationship with profit

performance. However, if banks hold too much equity with the very high ETA ratio, it suggests that

banks may lose the opportunity to gain more profits, so the ETA ratio may have a negative

relationship with the profit performance in those cases.

Figure 7 shows statistical summary of means of equity to total assets across banks in five

countries from 2004 to 2011. The largest ETA was in Philippines over the whole period due to the

improvement of its capital adequacy ratio (CAR) and bank of international settlements (BIS) (Bangko

Sentral ng Pilipinas, 2008). The second largest ETA was in Indonesia which was slightly below

-10

-5

0

5

10

15

20

25

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 %

YoY

)

Year

Figure 6: Percentage growth of return on asset(NIM) among countries

TH HK ID MY PH

10

Philippines. According to the percentage growth of ETA ratio in figure 8, Thailand was the most

outstanding with positive ratio over the period compared to the others because of the growth in capital

proportion and higher level of CAR requirement.

Source: Bankscope

Source: Bankscope

2

4

6

8

10

12

14

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure7: Means of equity to total asset (ETA) among countries

TH HK ID MY PH

-15-10-505

10152025

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 %

YoY

)

Year

Figure8: Percentage growth of equity to total assets (ETA) among countries

TH HK ID MY PH

11

(2) LTA = assets Total

Loans

(5)

Loans to total assets (LTA) are equal to total loans divided by total assets. LTA ratio is used

to explain how much banks lend the proceeds as a percentage of total assets. The higher LTA ratio

refers to the higher risk taking which may cause the higher default opportunity to the bank if they

have weak control and monitoring on the credit quality. This situation leads to lower profits and ends

up with negative relation to profit performance. On the other hand, if LTA ratio is higher with the

efficient credit monitoring and assessment, it implies that banks can provide better quality on the

assets and gain higher loan incomes and profits. In this case, LTA ratio may have a positive

relationship to profit performance.

Figure 9 shows statistical summary of means of loan to total assets across banks in five

countries from 2004 to 2011. For Thailand, LTA ratio was higher than other countries over all periods

but little decline from 2008 to 2009 because there was the global financial crisis and political problem

in Thailand. Therefore, banks were much pressured on the asset quality of loans lending, causing

them to prudently emphasize on risk and liquidity management. However, LTA ratio in Thailand

recovered in 2010 with response of the recovery of the world economy, which also increased loans

demand in both business loans and consumer loans (Bank of Thailand, 2007-2011). Regarding

percentage growth of LTA, figure 10 portrays that Indonesia continuously increased over all periods

and reached at 41.81 %YoY with the higher credit growth and fewer non-performing loans (Bank

Indonesia, 2006).

12

Source: Bankscope

Source: Bankscope

0

10

20

30

40

50

60

70

80

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure9: Means of loan to total assets (LTA) among countries

TH HK ID MY PH

-80

-60

-40

-20

0

20

40

60

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 %

YoY

)

Year

Figure 10: Percentage growth of loan to total assets (LTA) among countries

TH HK ID MY PH

13

(3) NPL/GL = loans gross

loans performingNon

(6)

Non-performing loans to gross loans (NPL/GL) are equal to non-performing loans divided by

gross loans. NPL/GL ratio is a standard proxy for the bank's asset risk and often used to measure asset

quality. Higher non-performing loans indicate that banks absorb more credit risks and poorer quality

of the assets which bring out to lower bank profitability. NPL/GL ratio should have a negative

relationship to profit performance.

Figure 11 illustrates statistical summary of means of non-performing loans to gross loans

across banks in five countries from 2004 to 2011. The lowest NPL/GL ratio was in Hong Kong which

stayed below 2 %YoY. Philippines, Thailand and Malaysia had continuously declined within parallel

over all periods because there was an improvement on debt restructure and credit quality which causes

lower non-performing loans, higher net income and higher bank profits. According to the percentage

growth of NPL/GL in figure12, most countries had a tendency to lower NPL/GL while Indonesia

confronted with the highest percentage growth of NPL/GL at 79.91 %YoY in 2005 in accordance

with the expansion of loans but contraction in credit quality.

Source: Bankscope

02468

101214161820

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure 11: Means of Non-Performing loans to gross loans (NPL/GL) among countries

TH HK ID MY PH

14

Source: Bankscope

(4) LA/CS = funding short term &Customer

asset Liquid

(7)

Liquid asset to Customer and short term funding (LA/CS) is equal to liquid asset divided by

customer & short term funding. LA/CS ratio is used to examine the percentage of customer and short

term funding that banks could manage when the customers withdraw money suddenly. Higher LA/CS

ratio can imply that banks have higher liquidity, lower opportunity to face the bank run, more chances

to generate incomes faster and gain more profits. In this case, LA/CS ratio may have a positive

relationship with profit performance. However, it may also cause negative relation between them in

the case when banks hold excessive liquid assets for a long time. This indicates that banks may face

the higher opportunity costs of investment on other assets with higher return. This provides a sign of

lower bank profits.

Figure 13 presents statistical summary of means of liquid asset to customer & short term

funding across banks in five countries from 2004 to 2011. LA/CS ratios in most countries are

fluctuated over the period whereas LA/CS ratio was quite steady in Thailand, implying that banks in

Thailand are able to manage their liquidity positions which bring benefit to meet its obligations and

maintain smooth banking operations. In figure 13, regarding the percentage growth of LA/CS,

Philippines had the highest liquidity growth over all periods due to reduction of reserve requirement

-100

-50

0

50

100

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 Y

oY%

)

Year

Figure 12: Percentage growth of non-performing loans to gross loans (NPL/GL) among countries

TH HK ID MY PH

15

and opening the dollar repurchase window to boost liquidity in the system after the global financial

crisis in 2008, which increase the growth ratio from 15.90 %YoY in 2008 to 29.71 %YoY in the

following year (Bangko Sentral ng Pilipinas, 2008).

Source: Bankscope

Source: Bankscope

0

5

10

15

20

25

30

35

40

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure 13: Means of liquid asset to Customer & short term funding (LA/CS) among countries

TH HK ID MY PH

-30-20-10

0102030405060

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

(20

04 =

0 Y

oY%

)

Year

Figure 14: Percentage growth of liquid asset to Customer & short term funding (LA/CS) among countries

TH HK ID MY PH

16

(5) LNSIZE = ln(total assets) (8)

Logarithm on total assets (LNSIZE) is mostly used as a proxy of bank size to see how much

of total asset which banks can acquire. More assets that banks can increase with the economies of

scale is more opportunities to increase their profits. Therefore, LNSIZE ratio should have a positive

relation to profit performance. However, banks may end up with negative relation they rise their

assets with the diseconomies of scale (Tingpetch, A.,2008).

Figure 15 shows statistical summary of means of logarithm on total assets across banks in five

countries from 2004 to 2011. Logarithm on total assets continuously increased in most countries over

the period within parallel upward trend. The largest total assets were in Hong Kong which varied from

10.35 %YoY in 2004 to 11.74 %YoY in 2011 because of an agreement of closer economic

partnership arrangement (CEPA) between Hong Kong and Mainland China on June 2003, which

encouraged banks to be eligible to expand their branch operations in Mainland with lower asset size

requirement (Hong Kong Monetary Authority, 2004). But the growth rate is smaller because of high

asset value base as shown in Figure 15.

Source: Bankscope

7

8

9

10

11

12

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure 15: Means of logarithm on total assets (LNSIZE) among countries

TH HK ID MY PH

17

Source: Bankscope

Macroeconomics variables

(6) INF = Inflation rate (9)

Inflation rate (INF) is measured by annual growth rate of the GDP implicit deflator (World

Bank). If the inflation rate is higher, the cost of capital or the interest rate also increases. This implies

that loan demands may decrease and lower the number of borrowers which leads to the funding

problem, and lower the profits to the bank. In this case, inflation rate should have negative relation to

profit performance.

Figure 16 presents statistical summary of means of inflation rate across banks in five

countries from 2004 to 2011. Inflation rate varied over the period, but in parallel for all countries. The

largest inflation rate was in Indonesia, which peaked at 13.11 %YoY in 2006 resulted from the

pressure on fuel price acceleration in October 2005 but started to decline since 2007. The second

largest inflation rate was in Philippines which accounted for 8.26 %YoY in 2008, followed by

Thailand, Malaysia and Hong Kong. Refering to Thailand, inflation rate dropped extremely and went

below Malaysia and Hong Kong from 5.4 %YoY to -0.85 %YoY in 2009 because of lower energy

price level and deceleration of food price compared with the previous year.

-202468

101214

2004 2005 2006 2007 2008 2009 2010 2011Per

cent

(20

04 =

0 Y

oY%

)

Year

Figure 16: Percentage growth of logarithm on total assets (LNSIZE) among countries

TH HK ID MY PH

18

Source: World Bank

(7) GDPG = GDP growth (10)

Gross domestic product growth (GDPG) is an essential factor which explains how well the

economy or firms (banks) developed so far. If GDPG ratio is high, it can imply that banks may

increase their profits by obtaining higher demand on borrowing and lending activities. Therefore,

GDPG might be positively related to profit performance.

Figure 17 presents statistical summary of means of GDP growth across banks in five

countries from 2004 to 2011. GDP decreased in all countries during the global financial crisis in 2008

and the effect of the slowdown of the economy was still remained in the following year. Most

countries in Asia especially Malaysia, Thailand and Hong Kong had negative change in GDPG, due to

lower private consumptions, investments, and slowdown in export sector. However, the state of

economy has improved after recovery since 2010 in developed countries including the US and

Eurozone which stimulates the recovery for developing countries afterwards.

-2

0

2

4

6

8

10

12

14

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure 16: Means of inflation rate (INF) among countries

TH HK ID MY PH

19

Source: World Bank

Country variables:

(8) Country variables (Cj) (11)

Country variables in Asia are represented as the vector of dummy variables by setting to 1 for

the observing countryj and 0 otherwise. The country dummy variables are used to identify whether

there are a significant difference among countries in Asia.

(9) Time Variables (Tj) (12)

Time variables in Asia are represented as the vector of dummy variables setting to 1 for the

observing timej and 0 otherwise. The time dummy variables are used to identify whether there are the

impact of timing effect in Asia.

Synchronization of business cycle

Figure 18 represents that there exists the positive relationship between average GDPG and

average ROA within parallel trend over the period. In figure 17, it illustrates that GDPG among five

countries dropped simultaneously during the global financial crisis period from 2008 to 2009. Also,

the average ratio of ROA follows the pattern of average GDPG, indicating that Asian banks are

-4

-2

0

2

4

6

8

10

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure 17: Means of GDP growth (GDPG) among countries

TH HK ID MY PH

20

significantly affected by the contraction in loan demands and external shocks from the crisis in

accordance with the decline in GDPG. Subsequently, business synchronization of Asian countries

started to recover since 2010 because of the improvement on business investment spending,

consumption and level of employment in those countries.

Source: World Bank

III. DATA DESCRIPTIONS

For this study, it explores Asian commercial banks in five countries such as Hong Kong,

Thailand, Indonesia, Malaysia, and Philippines from 2004 to 2011 because of the limited available

data previous to 2004. Bank’s specific variables are collected from Bankscope database, while

macroeconomics variables are collected from World Bank database. Bank observations includes only

commercial banks, excluding other financial institutes such as trust banks, financial companies,

savings institutions, and security companies, etc. Regarding the logarithm of total assets, it is a

measure of bank size whereas the numbers of banks are selected by the criteria of the ten largest total

assets for each country (Millions USD). However, the number of banks decline to 44 bank

observations (see Appendix A) because the study excludes the missing observations on those banks,

so the models panel data are strongly balanced, which reduces the problem of unobserved

heterogeneity.

00.20.40.60.811.21.41.61.8

-1012345678

2004 2005 2006 2007 2008 2009 2010 2011

Per

cent

Year

Figure18: Synchronization of business cycle

Average GDPG Average ROA

21

Table III represents the descriptive summary statistics for the dependent and independent

variables among Asian countries. The profitability as a proxy of ROE is relatively higher than ROA

and NIM for mean values and standard deviations. Further, the results show that ROE and ROA also

yield the negative values at -0.1333 (-13.33%) and -0.0075 (-0.75%), respectively.

Table III: Descriptive summary statistics used in the regression models Variables Mean St. Dev Min Max Obs ROE 0.1528 0.0655 -0.1333 0.3885 352

ROA 0.0137 0.0067 -0.0075 0.0458 352

NIM 0.0366 0.0167 0.0092 0.1104 352

ETA 0.0932 0.0268 0.0092 0.1104 352

LTA 0.5500 0.1439 0.0993 0.9090 352

NPL/GL 0.0547 0.0552 0.0009 0.5252 352

LA/CS 0.2502 0.0958 0.0120 0.6853 352

LNSIZE 9.7672 1.0586 7.3580 13.4900 352

INF 0.0461 0.0298 -0.0089 0.1311 352

GDPG 0.0489 0.0264 -0.0266 0.0847 352

Source: Bankscope and World Bank

IV. METHODOLOGY

5.1 Pearson Correlation

First of all, the model should be tested by Pearson correlation for all independent variables in

order to estimate whether there multicollinearity is existed among these variables or not ( (p-value

should not exceed 0.80), and to avoid the statistical problem on the regression model. For the results

in table IV, all of the independent variables are not significantly or not highly related to each other

which can imply that the estimated regressions do not face with multicollinearity problem in the

models tested.

22

Table IV: Pearson correlation coefficients

ETA LTA NPL/GL LA/CS LNSIZE INF LNGDPP ETA 1

LTA -0.3435 1

NPL/GL 0.1292 -0.1568 1

LA/CS 0.2453 -0.6923 -0.0887 1

LNSIZE -0.4279 0.1453 -0.3643 -0.0078 1

INF 0.1285 -0.2229 0.016 0.1313 -0.2165 1

LNGDPP -0.318 0.2595 -0.4409 0.0193 0.483 -0.2815 1 Notes: ETA: Equity to total assets ; LTA: Loans to total assets; NPL/GL: Non-performing loans to gross loans; LA/CS: Liquid asset to Customer & Short term funding ; LNSIZE: logarithm on total assets; INF: Inflation rate; LNGDPP: logarithm on GDP per capita

5.2 Panel model

According to panel model, the concerned problems comprise of heteroskedasticity, cross-

sectional dependence (contemporaneous correlation), serial correlation (autocorrelation) and non-

stationary of the data (Reyna, O.T.). Therefore, panel model analysis should be examined by using

four tests as follows:

(1) Testing for heteroskedasticity by modified Wald test with the null hypothesis of

homoskedasticity (constant variance). The result shows that we have faced with

heteroskedasticity problem, however, we can use the option of robust to control for

heteroskedasticity in both fixed and random effects. For the results in each model, there

are statistically significant at 95 percent confidence level or we reject the null hypothesis.

So it means we face with heteroskedasticity problem.

(2) Testing for cross-sectional dependence or contemporaneous correlation by Pesaran's test.

Null hypothesis is that the residuals are not correlated. The result is expected to be failed

to reject the hypothesis. For the results in each model, there are statistically insignificant

at 95 percent confidence level or we fail to reject the null hypothesis. This means there is

no problem on cross-sectional dependence, i.e. each bank is cross sectional independence.

(3) Testing for serial correlation or autocorrelation by Wooldridge test with the null

hypothesis of no serial correlation or first-order autocorrelation. It is expected that we will

23

not reject the null hypothesis. However, the results for each model rejects the null

hypothesis which means that we have an autocorrelation issue.

(4) Testing for unit roots or stationarity by LLC test with the null hypothesis; containing unit

root panel. The expected result is to reject the null hypothesis. LLC test is the most

suitable tool because panel of this study is strongly balanced as all variables provide the

data for all years. For the results in each model, there are statistically significant at 95

percent confidence level or we reject the null hypothesis. This means, panels do not

contain unit roots or panels are stationary.

In conclusion, the above tests show that we have two problems on heteroskedasticity and

serial correlation which can create the robust standard error estimates for linear panel models.

Consequently, the theory suggests that we can solve them by using cluster () option for panel with

fixed effect model.

5.2.1 Fixed effect estimator

y�� x��′ β� α� � ε�,ε�~IID�0,σ

��, i � 1,… , N, t � 1,… , N (13)

Where �� is the vector of explanatory variable, �� is the unknown intercept term for each

individual i or unobserved individual-specific effects. The subscription of�� tells that the intercepts of

each individual, which can be different but intercepts, does not vary over time, or it is called time-

invariant. And ε�ispresentedastheerrorterm. For the fixed effect model, in most cases, it seems

to be more appropriated due to higher possibly that unobserved individual-specific effects (��) are

correlated to independent variables ��.

5.2.2 Random effect estimator

y�� x��′ β� α� ε�� η�,η�~IID�0,σ

��, i � 1,… , N, t � 1,… , N (14)

Where�� '� �15�

24

The terms of � � '� is used instead of��, which is measured as the fixed effect. The study

assumes the random variable with the mean value of � (no subscript i at this time). And ω�� ε��

η�which is treated as a composite error. ε�� is a cross-section (individual-specific error component).

η� is the combined time series and cross-section error component (idiosyncratic term).For random

effect model, most unobserved individual-specific effects (��) are uncorrelated to independent

variables��.

5.2.3 Hausman test

Hausman test is performed to compare two estimators to detect whether the model is suitable

to be fixed effect or random effect. The models are performed by Hausman test with the null

hypothesis of “difference in coefficients, not systematic” or “individual effects are uncorrelated with

other independent variables in the model” If the results end up with the rejection of the null

hypothesis, the model should be performed by using fixed effect panel. Conversely, if the results end

up with the failed to reject of the null hypothesis, the model should be performed by using random

effect panel. The findings represent that all models result in random effect (see Appendix B).

Moreover, the problems of heteroskedasticity and autocorrelation in the models are able to fix by

using the cluster () option because it controls for the differences in variance and standard errors in the

group correlation.

5.3 An application of static panel model for bank performance on Asian commercial

banks

From the theoretical frameworks above, the study has applied the methodology from Kwan,

S.H.(2003) paper, then the regression models can be derived into three equations as follows:

)*+,� � � � -./,� � 0123)*,� � 43, � 56, � 7,� (16)

)*2,� � � � -./,� � 0123)*,� � 43, � 56, � 7,�(17)

891,� � � � -./,� � 0123)*,� � 43, � 56, � 7,� (18)

25

Where j represents the bank observations and t represents the year observations. )*+,� , )*2,�

:;<891,� are indicators of bank performance in term of profit performance; ./,� is presented as the

vector of bank’s specific variables; 123)*,� is represented as the vector of macroeconomics

variables; Cj is used as the vector of country specific dummy variables in five Asian countries; Tj is

used as the vector of time specific dummy variables in five Asian countries; 7,� is an error term; and α,

β, γ and δ are presented as regression coefficients.

Table V: Static panel model: estimation results of bank profitability Dependent variable Return on equity Return on asset Net interest margin Intercept 0.3002*** 0.0092 0.0441*** (-2.81) (0.95) (4.04) Net interest margin it 0.9912 0.1248* (1.47) (1.69) Equity to total assets it -0.3217 0.1063*** 0.0818***

(-1.23) (4.36) (3.13) Loans to total assets it -0.2144*** -0.0138** -0.0036 (-3.00) (-2.09) (-0.66) Non-Performing loans to gross loans it

-0.4387*** -0.0420*** -0.0116

(-3.21) (-3.61) (-1.28) Liquid asset to Customer & short term funding it

-0.0314 0.0009 -0.0072

(-0.38) (0.10) (-1.47) Logarithm on total assets it 0.0072 0.0006 -0.0018* (1.14) (1.22) (-1.73) Inflation rate it -0.1469* -0.0052 -0.0033 (-1.79) (-0.66) (-0.29) GDP growth it -0.1469 -0.0138 0.0234** (-1.32) (-1.34) (1.96) Hong Kong -0.0433 -0.0028 -0.0135*** (-1.58) (-1.16) (-5.61) Indonesia -0.0270 -0.0025 0.0250*** (-0.98) (-0.89) (4.71) Malaysia 0.0016 0.0006 0.0011 (0.07) (0.37) (0.32) Philippines -0.0643*** -0.0040* 0.0017 (-2.76) (-1.75) (0.55) Crisis(2008-2009) -0.0596*** -0.0060*** 0.0047*** (-4.33) (-4.51) (3.25) Non-Crisis -0.0300*** -0.0030*** 0.0025*** (-3.11) (-3.35) (2.80) Adjusted R-square 0.3323 0.5131 0.7435 Number of banks 44 44 44 Number of observations 352 352 352 Notes: The digits in parentheses represent t-statistic value in static panel model with fixed effect. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

26

Table V represents the estimated results of bank profitability by using static panel model.

The regression models are categorized into four groups which consist of bank’s specific variables,

macroeconomics variables, country dummy variables and time dummy variables. The adjusted R-

squares, which are used to explain the goodness of fit or the explanatory power to the model, are

considerably high at the satisfactory level at 0.3323, 0.5131 and 0.7435 for ROE, ROA and NIM,

respectively. The empirical evidences can be summarized as follows:

(1) Bank’s specific variables (t):

Because ROE and ROA are derived from net income items, whereas NIM is derived from

difference between interest revenues and interest expenses (net interest income), it seems to have

linear relationships to the net income. Therefore, the study aims to test whether NIM itself can be

used as an explanatory variable to explain ROE and ROA. The finding shows that NIM is

statistically significant to ROA, but only at 10% significance level whereas ROE is not significant,

indicating that NIM has no strong impact on ROE and ROA.

The empirical evidence finds that ETA has positively and statistically significant to ROA

and NIM. The result is consistent with the earlier studies of Demirgüç-Kunt, A. and Huizinga, H.

(1999) Sufian, F. and Chong, R.R. (2008); and Sufian, F. and Mohamad Akbar Noor Mohamad

Noor. (2012), suggesting that banks with high equity have more opportunities to lower cost of

funding due to lower cost of bankruptcy. Moreover, higher ETA is beneficial to sustain capital

strength to the bank which encourages banks’ activities and services, also increases profits to the

bank so far.

According to the ratio of LTA, the findings represent the negative sign related to ROE and

ROA which supported the study of Staikouras, C. and Wood, G.E. (2004) and Kyriaki K., Pasiouras,

F. and Tsaklanganos, A. (2007), implying that intensive loans on bank’s assets do not coincide with

higher level of profitability, thanks to the proportion of loans do not simultaneously increase with the

improvement of credit quality. In addition, higher LTA refers that banks can provide higher liquidity

27

from bank loans to the customers, but these assets are generally related to lower rate of return.

Regarding the measurement of credit quality of NPL/GL, there is negatively and statistically

significant related to ROE and ROA, which strongly supports the negative hypothesized relation of

this study, indicating that banks with higher NPL/GL have more chances on credit defaults from

lending activities and tend to lower level of profitability, resulted from the weakness on loan quality

and credit monitoring.

For LNSIZE, the variables are used as a proxy of bank size. The results show a negative sign

correspond to NIM ratio. This implies that Asian banks with large total assets size have less

capability to manage their assets structure with the economy of scale. Nevertheless, the results

examine that there is no significant impact between LA/CS and all of the dependent variables.

(2) Macroeconomics variables at time (t):

Both Inflation rate and gross domestic product growth result in the correct hypothesized

sign. Inflation rate is statistically significant to ROE ratio with negative sign, similarly to the study of

Sufian, F. and Chong, R.R. (2008), implying that banks have a tendency to face with faster increase

in the operating costs than the growth of bank revenues, this brings about the reduction on bank

profits. For the measurement of economic development of GDPG, the ratio is positively and

statistically significant to NIM ratio. The empirical results are consistent with the paper of

Staikouras, C. and Wood, G.E. (2004), indicating that the countries with higher GDPG are able to

sustain their economic growth, which is essential to boost more loan demand and supply into the

financial system, also beneficial to rise the profits to the banks.

(3) Country dummy variables (t):

The study tests whether there are bank profit differences among countries by using least

square dummy variables, similar to the study of Kwan, S. (2003). For identification purpose, the

dummy variable for Thailand is excluded so that the estimated coefficients are able to measure the

bank profit of the four Asian countries, Hong Kong, Indonesia, Malaysia and Philippines, relative to

28

Thailand (base country). The estimated coefficients explain how much bank profits in Thailand is

affected when bank profits in Hong Kong, Indonesia, Malaysia and Philippines increases by one unit.

The empirical results show that the portions of the country dummy variables related to ROE

and ROA of Philippines are significantly lower than Thailand, suggesting that Thai bank is

outperformed Philippine bank in terms of profitability management with its average ROE and ROA

higher than Philippines'. For the case of Hong Kong, the estimated coefficient of country dummy

variables related to NIM is significantly lower than Thailand. This implies that Thai bank is able to

extract higher monopoly rents from the interest rate spread. While banking system of Hong Kong has

more competitive environment which has lower possibility to charge very high price loans. Also,

total asset as a proxy of bank size of Hong Kong compared to Thailand is significantly lower

(Figure5). For the case of Indonesia, the estimated coefficient of country dummy variables

correspond to NIM is significantly higher than Thailand. The results suggest that the banking system

of Indonesia is under the condition of low competition associated with the market structure (Laeven,

L., 2007). With this condition, more potential power of setting higher prices in loan markets is led, so

banks are able to obtain more profits from higher interest rate spread. However, the portions of the

country dummy variables in Malaysia are not statistically significant to Thailand.

(4) Time dummy variables (t):

The study tests whether there are timing effects over the periods in term of bank profits by

using least square dummy variables, similarly to the study of Kwan, S. (2003). For identification

purpose, the dummy variable for 2004 is excluded to let the estimated coefficients measure the profit

performance of the remaining years since 2005 to 2011 relative to 2004 (base year). The estimated

coefficients explain how much bank profits in 2004 will be changed when bank profits from year

2005 to 2011 increases by one unit. Considering the global financial crisis, the period of the study is

categorized into two groups which are dummy variables for crisis period (2008 -2009) and dummy

variables for non-crisis period (2005-2007 and 2010-2011).

29

The empirical evidence finds that the portions of the time dummy variables for non-crisis

period are negatively and statistically significant to both ROE and ROA with the same value,

accounting for -0.0300 at 1% level. Conversely, the portions of the time dummy variables for

non-crisis period are positively and statistically significant to NIM only, accounting for 0.0025 at 1%

level.

Regarding the time dummy variables for crisis period, the estimated coefficients are

relatively lower than the ones with non-crisis period for both ROE and ROA, because of the decline

in loan demand and the slowdown of the economy. However, the estimated coefficients are still

higher than the ones with non-crisis period for NIM, with the improvement of 0.0047 at 1% level.

In addition, the estimated coefficients of the dummy variables for the crisis period show the

negative sign to ROE and ROA, but they show positive sign to NIM. Interestingly, the ratios of NIM

remain unchanged over the period for all countries during the global financial crisis in 2008 (Figure

5), suggesting that banks are more capable to continue their business in line with their past

experiences, also able to diversify risk to sustain the level of demand for loans and deposits for the

customers. Therefore, banks receive higher interest spread and higher profits during the crisis.

5.4 An application of static panel model with lagged independent variables for

bank performance on Asian commercial banks

The purpose of the study is to test whether there are differences in the determinants that

explain the bank performances across countries, especially the ones with and without lagged term, l

then the regression models can be derived as follows:

)*+,� � � � -./,�=> � 0123)*,�=> � 43, � 56,�7,� (19)

)*2,� � � � -./,�=> � 0123)*,�=> � 43, � 56, �7,�(20)

891,� � � � -./,�=> � 0123)*,�=> � 43, � 56, �7,�(21)

30

Where j represents the bank observations and t represents the year observations.

)*+,� , )*2,�:;<891,� are indicators of bank performance in term of profit performance; ./,�=>is

presented as the vector of lagged bank’s specific variables; 123)*,�=> is represented as the vector

of lagged macroeconomics variables; Cj-1 is used as the vector of country specific dummy variables

in five Asian countries; Tj-1 is used as the vector of time specific dummy variables in five Asian

countries;7,� is an error term; and α, β, γ and δ are presented as regression coefficients.

Table VI: Static panel model with lagged dependent variables: estimation results of bank profitability Dependent variable Return on equity Return on asset Net interest margin

Intercept 0.2405** 0.0056 0.0446***

(2.57) (0.69) (3.81)

Net interest margin it -1 0.5999 0.0685 (1.00) (1.31)

Equity to total assets it -1 -0.4574** 0.0745*** 0.0625*

(-2.02) (3.76) (1.93)

Loans to total assets it -1 -0.1343* -0.0058 0.0078

(-1.79) (-0.77) (1.28)

Non-Performing loans to gross loans it -1

-0.3052*** -0.0311*** -0.0019

(-3.41) (-4.51) (-0.33) Liquid asset to Customer & short term funding it-1 -0.0493 -0.0021 -0.0041

(-0.81) (-0.28) (-0.69) Logarithm on total assets it-1 0.0052 0.0005 -0.0023**

(0.87) (0.96) (-2.41)

Inflation rate it -1 -0.1189 -0.0033 0.0217*

(-1.27) (-0.36) (1.90) GDP growth it-1 0.0963 0.0063 0.0015 (1.32) (0.90) (0.19) Hong Kong -0.0243 -0.0014 -0.0111*** (-1.01) (-0.69) (-3.95) Indonesia -0.0142 -0.0001 0.0257*** (-0.58) (-0.02) (4.57) Malaysia 0.0182 0.0017 0.0016 (0.95) (0.98) (0.44) Philippines -0.0310*** 0.0003 0.0044 (-1.21) (0.12) (1.27) Crisis(2008-2009) -0.0235*** -0.0027*** 0.0011** (-4.86) (-5.78) (2.16) Non-Crisis (omitted) (omitted) (omitted) Adjusted R-square 0.2895 0.3994 0.7165 Number of banks 44 44 44 Number of observations 308 308 308 Notes: The digits in parentheses represent t-statistic value in static panel model with lagged dependent variables. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

31

Table VI presents the estimation results of bank profitability by using static panel model with

lagged independent variables in order to control the past performances. The study aims to provide the

results of determinants of lagged independent variables (t-1) compared to the ones without lagged

term (t) to see whether there are the different findings on the determinants of bank profitability.

Referring to bank’s specific variables, the empirical evidences show that all of the lagged

determinants are statistically significant to bank profits with the same hypothesized sign as the ones

without lagged term, suggesting that there are indifferences in the period of time on the determinants

of bank profits. Regarding to macroeconomic variables, only lagged inflation rate is statistically

significant to NIM. The ratio turns to be a positive sign. This implies that the increase in lagged

inflation rate is able to stimulate more incomes rather than costs to the bank, also the inflation rate

affect to raise the interest rate associated with higher expected return and higher profits to the bank.

Further, this implies there is a slow response to the change of macroeconomics factor which explains

the level of bank profitability. According to the country dummy variables, the empirical results are

similar to the table V which examines that all of Asian countries, except Malaysia, are statistically

significant to Thailand. For the time dummy variables, the estimated coefficients of the dummy

variables for the ones with crisis period are as same as the ones without, but a limitation of

comparison between the two periods exists because of the collinearity of the time dummy variables in

panel regression.

V. CONCLUSION

The study focuses on determinants of bank performance in term of profitability of Asian

commercial banks among five countries; Hong Kong, Thailand, Indonesia, Malaysia and Philippines.

There are four main categories of determinants of bank performance which consist of bank’s specific

variables, macroeconomics variables, country dummy variables and time dummy variables.

Furthermore, the determinants are separated into two differences period of time; defining to the

current period (t) and the previous year (t-1) which refer to lagged independent variables.

32

The results of bank’s specific variables with lagged term are similar to the ones without. The

findings represent that equity to total assets has a positive significant impact on bank performances.

In contrary, loans to total assets, non-performing loans to gross loans and logarithm of total assets

have negative significant impact on bank performances. Nevertheless, liquidity asset to customer

deposits and short term funding do not have significant effect on bank performances. Referring to

Macroeconomics variables, gross domestic product growth have a positive significant impact on bank

performances in the current year (t), but inflation rate has a negative significant impact on bank

performances only in the lag of one year (t-1) because of slower response of macroeconomics factor.

Regarding the country dummy variables, Thailand is significantly higher than Philippines for

the ratios of both return on equity and return on assets, also higher than Hong Kong for the net interest

margin because of higher opportunities on the increasing interest rate spread as well as lower

competitive environment in the market structure. Conversely, Thailand is significantly lower than

Indonesia for the net interest margin. According to the time dummy variables, the dummy variables

for both non-crisis and crisis period have negative significant impact on return on equity and return on

asset but positive on net interest margin. It is obviously seen that the dummy variables in crisis period

are relatively lower than the ones with non-crisis period due to the recession from the global financial

crisis in 2008.

The findings of this study are able to bring benefits to the bank itself, the regulators, as well as

the researchers who are interested in the related issue. Further study may include several other factors

which also significantly impact on explaining bank performance such as non –interest income, capital

adequacy ratio (CAR) and unemployment rate, etc. This issue should be mentioned in future work. In

addition, there will be an existing new challenging subject towards the regional integration of Asean

Economic Community (AEC) in 2015 which affect to the changes of regulations, policies, and

strategies to the financial industry over ASEAN countries unavoidably. Consequently, the study of

bank performance is very important in order to sustain the profitability to the bank and the stability to

the economy.

33

REFERENCES

1. Arner, D.W. and Park, C.Y., 2010. Global Financial Regulatory Reforms: Implications for

Developing Asia, Asian Development Bank Working Paper Series on Regional Economic

Integration No. 57

2. Bank Indonesia, Economic Report on Indonesia, 2004-2011. Department of Economic

Research and Monetary Policy.

3. Bank of Thailand, Report/Press Release on Performance of the Banking System, 2004-2011.

Thailand’s Banking System Performance.

4. Bangko Sentral ng Pilipinas (BSP), BNM Annual Report, 2008. A Status Report on the

Philippine Financial System.

5. Demirgüç-Kunt, A. and Huizinga, H., 1999. Determinants of commercial bank interest

margins and profitability: Some international evidence. World Bank Economic Review, 13(2),

379-408.

6. Demirgüç-Kunt, A. and Huizinga, H., 2000. Financial Structure and Bank Profitability. World

Bank Policy Research Working Paper No. 2430.

7. Heffernan, S. and Fu, M., 2008. The Determinants of Bank Performance in China.

EMG Working Paper Series No. 032008.

8. Hong Kong Monetary Authority (HKMA), Annual Report, 2004, Publications & Research.

9. Kyriaki K., Pasiouras, F. and Tsaklanganos, A., 2007. Domestic and multinational

determinants Fof foreign bank profits: The case of Greek banks operating abroad. Journal of

Multinational Financial Management, 17, 1-15.

10. Kwan, S., 2003. Operating performance of banks among Asian economies: An international

and time series comparison. Journal of Banking and Finance, 27(3), 471–489.

11. Laeven, L., 2007. Banking sector performance in East Asian countries: The effects of

competition, diversification, and ownership. World Bank.

12. Lee,C.C. and Hsieh,M.F., 2013. The impact of bank capital on profitability and risk in Asian.

Journal of International Money and Finance, 32, 251-258.

34

13. Liu, Mengque., 2010. The Hausman test in dynamic panel model. Uppsala University,

Department of Statistics.

14. Pavabutr, P. and Sirodom,K., 2010. Mutual fund flows in Asian Equity markets. Capital

Market Research Institute, The Stock Exchange of Thailand.

15. Promyod, P., 2012. The determinants of bank margins in Thailand. Working paper,

Department of Finance, Thammasat University.

16. Naceur, S.B. and Omran,M., 2011. The Effects of Bank Regulations, Competition, and

Financial Reforms on Banks' Performance. Emerging Market Review,12, 1-20.

17. Staikouras, C. and Wood, G.E., 2004. The Determinants of European Bank Profitability.

International Business and Economics Research Journal, 3(6).

18. Sufian, F. and Chong, R.R., 2008. Determinants of Bank Profitability in a Developing

Economy: Empirical Evidence from the Philippines. Journal of Accounting and Finance, 4(2),

91-112.

19. Sufian, F. and Mohamad, A.N. Noor, 2012. Determinants of Bank Performance in a

Developing Economy: Does Bank Origins Matters? Global Business Review, 13(1), 1-23.

20. Tingpetch, A., 2008. Determinants of Profitability of Thai Commercial banks. Working paper,

Department of Finance, Thammasat University.

35

APPENDIX A

Lists of banks among countries

Hong Kong

1. Hongkong and Shanghai Banking Corporation Limited

2. Bank of China (Hong Kong) Limited

3. Hang Seng Bank Ltd.

4. Bank of East Asia Ltd.

5. Industrial and Commercial Bank of China (Asia) Limited

6. DBS Bank (Hong Kong) Limited

7. Nanyang Commercial Bank Ltd.

8. Wing Hang Bank Ltd

9. CITIC Bank International Limited

Thailand

1. Bangkok Bank Public Company Limited

2. Krung Thai Bank Public Company Limited

3. Siam Commercial Bank Public Company Limite

4. Kasikornbank Public Company Limited

5. Government Savings Bank

6. Bank of Ayudhya Public Company Ltd.

7. Thanachart Bank Public Company Limited

8. TMB Bank Public Company Limited

9. Government Housing Bank

36

Indonesia

1. Bank Mandiri (Persero) Tbk

2. Bank Central Asia

3. Bank Negara Indonesia (Persero)

4. PT Bank CIMB Niaga Tbk

5. Bank Danamon Indonesia Tbk

6. Bank Pan Indonesia Tbk PT

7. Bank Permata Tbk

8. Bank Internasional Indonesia Tbk

9. Bank Tabungan Negara (Persero)

Malaysia

1. Public Bank Berhad

2. CIMB Bank Berhad

3. RHB Bank Berhad

4. AmBank (M) Berhad

5. HSBC Bank Malaysia Berhad

6. Bank Kerjasama Rakyat Malaysia Berhad

7. United Overseas Bank (Malaysia) Bhd.

8. OCBC Bank (Malaysia) Berhad

37

Philippines

1. BDO Unibank Inc

2. Metropolitan Bank & Trust Company

3. Bank of The Philippine Islands

4. Land Bank of the Philippines

5. Rizal Commercial Banking Corp.

6. Philippine National Bank

7. Union Bank of the Philippines

8. China Banking Corporation

9. Security Bank Corporation

38

APPENDIX B

Stata Output for Hausman Test

From the equation (16), the model represents that chi-square provides 3.09 test statistic and p-value at

0.9995, so the model is significantly related to random effect model.

PPPPrrrroooobbbb>>>>cccchhhhiiii2222 ==== 0000....9999999999995555 ==== 3333....00009999 cccchhhhiiii2222((((11115555)))) ==== ((((bbbb----BBBB))))''''[[[[((((VVVV____bbbb----VVVV____BBBB))))^̂̂̂((((----1111))))]]]]((((bbbb----BBBB))))

TTTTeeeesssstttt:::: HHHHoooo:::: ddddiiiiffffffffeeeerrrreeeennnncccceeee iiiinnnn ccccooooeeeeffffffffiiiicccciiiieeeennnnttttssss nnnnooootttt ssssyyyysssstttteeeemmmmaaaattttiiiicccc

BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ....0000000044441111888844449999 ....0000000011111111888822222222 ....0000000033330000000022228888 ....0000000033333333222244444444 YYYY6666 ----....000022225555777766668888 ----....0000333322227777666633337777 ....0000000066669999999955558888 ....0000000066661111888811112222 YYYY5555 ----....000011119999666644442222 ----....0000222288889999777755554444 ....0000000099993333333333334444 ....0000000077775555333322222222 YYYY4444 ....0000000033338888999944444444 ----....0000000055553333777744446666 ....000000009999222266669999 ....0000000099992222999999993333 YYYY3333 ----....0000000000001111111166665555 ----....0000111111112222666611115555 ....000011111111111144445555 ....0000111111111111111111119999 YYYY2222 ....0000222277774444000077771111 ....000011112222111133333333 ....0000111155552222777744441111 ....0000111144448888444499993333 YYYY1111 ....0000444488882222222299994444 ....0000333311110000888888889999 ....0000111177771111444400005555 ....0000111177774444111133333333 ggggddddppppggggrrrroooowwwwtttthhhh ----....1111666655554444000055558888 ----....1111666611115555444455559999 ----....0000000033338888555599999999 ....0000222266667777666600005555 iiiinnnnffff ----....1111222244446666999911118888 ----....1111555522220000666666662222 ....0000222277773333777744444444 ....0000333399998888777711113333 llllnnnnssssiiiizzzzeeee ....0000222266669999111100007777 ....000000008888999922229999 ....0000111177779999888811118888 ....000011115555333300008888 llllaaaaccccssss ----....0000222200006666666655554444 ----....0000111188884444222244445555 ----....000000002222222244441111 ....0000111122222222333333336666 iiiillllggggllll ----....4444333388882222888877776666 ----....4444555500005555888833334444 ....0000111122222222999955559999 ....0000222288889999777711115555 llllttttaaaa ----....2222333344446666000022227777 ----....2222111155557777555599997777 ----....000011118888888844443333 ....0000222288882222111100008888 eeeettttaaaa ----....2222111166661111333300004444 ----....3333333366660000111111116666 ....1111111199998888888811112222 ....11110000666622223333 nnnniiiimmmm ....99991111000066669999 1111....000099999999999911116666 ----....1111888899992222222266662222 ....3333333311110000222222221111 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss

.... hhhhaaaauuuussssmmmmaaaannnn ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm

39



From the equation (18), the model represents that chi-square provides 17.45 test statistic and p-value

at 0.1795, so the model is significantly related to random effect model.

PPPPrrrroooobbbb>>>>cccchhhhiiii2222 ==== 0000....9999999988889999 ==== 3333....55553333 cccchhhhiiii2222((((11115555)))) ==== ((((bbbb----BBBB))))''''[[[[((((VVVV____bbbb----VVVV____BBBB))))^̂̂̂((((----1111))))]]]]((((bbbb----BBBB))))


BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ....0000000000004444000066663333 ....0000000000001111333388883333 ....000000000000222266668888 ....0000000000003333111100008888 YYYY6666 ----....0000000033333333111111117777 ----....0000000033338888777722227777 ....000000000000555566661111 ....0000000000005555666699999999 YYYY5555 ----....0000000011117777888855553333 ----....0000000022225555666655558888 ....0000000000007777888800005555 ....0000000000006666999900004444 YYYY4444 ----....0000000000003333222266666666 ----....0000000011111111555555559999 ....0000000000008888222299993333 ....0000000000008888555533335555 YYYY3333 ....0000000000003333000022222222 ----....0000000000007777111199997777 ....0000000011110000222211119999 ....0000000011110000111188886666 YYYY2222 ....0000000022227777999911113333 ....0000000011113333999977775555 ....0000000011113333999933338888 ....0000000011113333555599991111 YYYY1111 ....0000000044446666222277774444 ....0000000033330000555599995555 ....0000000011115555666677779999 ....0000000011115555999944443333 ggggddddppppggggrrrroooowwwwtttthhhh ----....0000222200005555222233336666 ----....0000111199993333999966662222 ----....0000000011111111222277773333 ....0000000022226666555522225555 iiiinnnnffff ----....0000111122229999000077777777 ----....0000111144444444555500004444 ....0000000011115555444422227777 ....000000003333777755554444 llllnnnnssssiiiizzzzeeee ....0000000022224444777777773333 ....0000000000008888111177771111 ....0000000011116666666600002222 ....000000001111333399997777 llllaaaaccccssss ....0000000022228888555577775555 ....0000000022223333888899995555 ....0000000000004444666677779999 ....0000000011111111777755557777 iiiillllggggllll ----....0000444400009999444433335555 ----....0000444422228888666600002222 ....0000000011119999111166666666 ....0000000022227777444400007777 llllttttaaaa ----....0000111144446666000077776666 ----....0000111133338888111144449999 ----....0000000000007777999922227777 ....0000000022226666444466665555 eeeettttaaaa ....1111000077771111777766664444 ....111100004444222211112222 ....0000000022229999666644444444 ....0000000099999999000066667777 nnnniiiimmmm ....1111222266665555444444443333 ....111133337777222200007777 ----....0000111100006666666622227777 ....0000333300009999333344445555 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss


((((VVVV____bbbb----VVVV____BBBB iiiissss nnnnooootttt ppppoooossssiiiittttiiiivvvveeee ddddeeeeffffiiiinnnniiiitttteeee)))) PPPPrrrroooobbbb>>>>cccchhhhiiii2222 ==== 0000....1111777799995555 ==== 11117777....44445555 cccchhhhiiii2222((((11113333)))) ==== ((((bbbb----BBBB))))''''[[[[((((VVVV____bbbb----VVVV____BBBB))))^̂̂̂((((----1111))))]]]]((((bbbb----BBBB))))


BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ----....0000000011113333444488887777 ....000000002222444455558888 ----....0000000033338888000066666666 ....0000000011115555999900006666 YYYY6666 ....0000000000002222111111118888 ....0000000044440000333322223333 ----....0000000033338888222200004444 ....0000000011114444888877779999 YYYY5555 ....0000000011113333444466667777 ....0000000055551111000000002222 ----....0000000033337777555533335555 ....0000000011113333666644449999 YYYY4444 ....0000000011115555222222227777 ....0000000055554444000066664444 ----....0000000033338888888833337777 ....0000000000002222111155554444 YYYY3333 ....0000000011114444333366665555 ....0000000055552222999955558888 ----....0000000033338888555599994444 .... YYYY2222 ....0000000011111111333300003333 ....0000000044449999777755556666 ----....0000000033338888444455553333 .... ggggddddppppggggrrrroooowwwwtttthhhh ----....0000000022221111222200002222 ----....0000000022229999000066668888 ....0000000000007777888866666666 .... iiiinnnnffff ....0000000077776666888866667777 ....000011110000999955559999 ----....0000000033332222777722223333 ....000000002222222200004444 llllnnnnssssiiiizzzzeeee ....0000000011114444000088885555 ....000000001111222200003333 ....0000000000002222000055555555 ....0000000011112222444455556666 llllaaaaccccssss ----....0000000055554444 ----....0000000066660000222244446666 ....0000000000006666222244446666 ....0000000000003333777700006666 iiiillllggggllll ----....0000111166668888555577774444 ----....0000111199993333777755556666 ....0000000022225555111188882222 ....0000000011115555000066666666 llllttttaaaa ....0000000033331111999944449999 ....000000004444999900008888 ----....0000000011117777111133331111 ....000000001111666644442222 eeeettttaaaa ....0000888899995555222299995555 ....1111000022229999555522223333 ----....0000111133334444222222228888 ....0000000077771111888800006666 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss


40

From the equation (19), the model represents that chi-square provides 10.52 test statistic and p-value

at 0.6511, so the model is significantly related to random effect model.





BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ....0000111155553333999955552222 ....0000111144446666333366667777 ....0000000000007777555588885555 ....0000000077773333888855552222 YYYY6666 ----....0000000055558888555544441111 ----....0000000066665555222266664444 ....0000000000006666777722223333 .... YYYY5555 ----....0000333300006666888855555555 ----....0000222299991111555599991111 ----....0000000011115555222266663333 ....0000000011118888888811117777 YYYY3333 ----....0000111111118888333377779999 ----....0000000022229999999900006666 ----....0000000088888888444477773333 .... YYYY2222 ----....0000000066661111111177771111 ....0000000066663333222200007777 ----....0000111122224444333377777777 .... LLLL....ggggddddppppggggrrrroooowwwwtttthhhh ....2222888811112222333344444444 ....2222777766660000000000001111 ....0000000055552222333344444444 ....0000222244440000777777773333 LLLL....iiiinnnnffff ----....2222444411117777333344446666 ----....1111777799997777555566667777 ----....0000666611119999777777779999 ....0000333399999999444466663333 LLLL....llllnnnnssssiiiizzzzeeee ----....000011112222111111116666 ....0000000055552222222233333333 ----....0000111177773333333399993333 ....0000111166661111777755558888 LLLL....llllaaaaccccssss ----....0000444422220000444444448888 ----....0000444455555555777711114444 ....0000000033335555222266666666 ....0000111133330000333377776666 LLLL....iiiillllggggllll ----....2222444400009999666611113333 ----....2222888877777777666622221111 ....0000444466668888000000009999 ....000022229999444488886666 LLLL....llllttttaaaa ----....111122224444777733337777 ----....1111555511113333555577774444 ....0000222266666666222200004444 ....0000333300008888000044446666 LLLL....eeeettttaaaa ----....5555000088888888111155551111 ----....4444222277774444555511116666 ----....0000888811113333666633334444 ....1111000088889999999966663333 LLLL....nnnniiiimmmm ....3333000022228888999999995555 ....6666333388887777888833338888 ----....3333333355558888888844443333 ....3333333300002222222288886666 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss




BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ....0000000022229999555599995555 ....000000002222333333337777 ....0000000000006666222222225555 ....0000000000006666999922225555 YYYY6666 ....0000000000000000444466669999 ----....0000000000006666222200002222 ....000000000000666666667777 .... YYYY5555 ----....0000000033331111444433337777 ----....0000000033336666666655559999 ....0000000000005555222222221111 ....0000000000001111888811111111 YYYY3333 ----....0000000000004444555544447777 ----....0000000000003333444400004444 ----....0000000000001111111144443333 .... YYYY2222 ....0000000000001111444400003333 ....0000000000006666222222225555 ----....0000000000004444888822222222 .... LLLL....ggggddddppppggggrrrroooowwwwtttthhhh ....0000333355550000111188882222 ....0000333355556666777777774444 ----....0000000000006666555599992222 ....0000000022220000999944449999 LLLL....iiiinnnnffff ----....0000111122226666111177771111 ----....0000000055556666111177772222 ----....0000000066669999999999999999 ....0000000033336666888844443333 LLLL....llllnnnnssssiiiizzzzeeee ----....0000000011112222111100002222 ....0000000000003333555500006666 ----....0000000011115555666600008888 ....0000000011115555333333331111 LLLL....llllaaaaccccssss ----....0000000000001111888899992222 ----....0000000011111111333344449999 ....0000000000009999444455558888 ....0000000011112222333322228888 LLLL....iiiillllggggllll ----....0000222244440000666633332222 ----....000022228888666600005555 ....0000000044445555444411118888 ....0000000022228888000099997777 LLLL....llllttttaaaa ----....0000000055550000333366667777 ----....0000000088887777222233332222 ....0000000033336666888866665555 ....000000002222999944441111 LLLL....eeeettttaaaa ....0000666611114444222222225555 ....0000777766668888888833333333 ----....0000111155554444666600007777 ....0000111100003333888888884444 LLLL....nnnniiiimmmm ....0000333311115555888844448888 ....000077778888222244445555 ----....0000444466666666666600002222 ....0000333311115555555522229999 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss


41





BBBB ==== iiiinnnnccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHaaaa,,,, eeeeffffffffiiiicccciiiieeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg bbbb ==== ccccoooonnnnssssiiiisssstttteeeennnntttt uuuunnnnddddeeeerrrr HHHHoooo aaaannnndddd HHHHaaaa;;;; oooobbbbttttaaaaiiiinnnneeeedddd ffffrrrroooommmm xxxxttttrrrreeeegggg YYYY7777 ....0000000044447777666600009999 ....0000000055552222444444442222 ----....0000000000004444888833333333 ....0000000000001111333355557777 YYYY6666 ....0000000022226666555522222222 ....0000000033332222666611119999 ----....0000000000006666000099997777 ....0000000000003333333355553333 YYYY5555 ....0000000033335555777755553333 ....0000000044444444222222226666 ----....0000000000008888444477773333 ....0000000000004444444488889999 YYYY4444 ....0000000033330000222299995555 ....0000000044440000888866663333 ----....0000000011110000555566668888 ....0000000000005555888888881111 YYYY3333 ....0000000022221111777755552222 ....000000003333777744446666 ----....0000000011115555777700009999 ....0000000000008888999988883333 YYYY2222 ....0000000011116666333300005555 ....0000000033335555777755551111 ----....0000000011119999444444446666 ....0000000011110000888822227777 LLLL....ggggddddppppggggrrrroooowwwwtttthhhh ....0000333366667777777700009999 ....0000333355557777444422221111 ....0000000011110000222288888888 .... LLLL....iiiinnnnffff ....000033331111777711112222 ....0000333388883333555555557777 ----....0000000066666666444433337777 ....000000000000666600007777 LLLL....llllnnnnssssiiiizzzzeeee ----....0000000022222222444400003333 ----....0000000000003333000077778888 ----....0000000011119999333322225555 ....0000000011112222222277776666 LLLL....llllaaaaccccssss ----....0000000022225555777711111111 ----....0000000033334444888899999999 ....0000000000009999111188888888 ....0000000000002222666677774444 LLLL....iiiillllggggllll ----....0000000022223333000055556666 ----....0000000055554444222277776666 ....0000000033331111222211119999 ....0000000011111111777788887777 LLLL....llllttttaaaa ....000011113333000033331111 ....0000111122226666777766666666 ....0000000000003333555544444444 ....0000000011115555999911116666 LLLL....eeeettttaaaa ....0000555555558888111166668888 ....000077775555888844442222 ----....0000222200000000222255552222 ....0000000066664444222255558888 ffffiiiixxxxeeeedddd rrrraaaannnnddddoooommmm DDDDiiiiffffffffeeeerrrreeeennnncccceeee SSSS....EEEE.... ((((bbbb)))) ((((BBBB)))) ((((bbbb----BBBB)))) ssssqqqqrrrrtttt((((ddddiiiiaaaagggg((((VVVV____bbbb----VVVV____BBBB)))))))) CCCCooooeeeeffffffffiiiicccciiiieeeennnnttttssss


Documents

DETERMINANTS OF BANK PERFORMANCE ON ASIAN …